Dong Chen

Columbus, Ohio has 13 percent growth in company formation YoY. We also just passed Chicago as the 5th largest concentration of data centers worldwide. Construction is accelerating. #ohiotech -

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I wrote an article on how we translate user-generated content at scale at Mercari. It summarizes two years of work with various models, LLMs but not only, and how cost evolved, reducing it 100x over the period. I also go over non-AI features and user experience. https://lnkd.in/gbpQAQmX

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My Startup Was Working… Until It Wasn’t We had paying customers. We had local press. We were gaining traction. It was 2016. Then UberEats, DoorDash, and Postmates entered Portland with billions in funding. At the same time, Intel—where I worked full-time—announced massive layoffs. Overnight, my 40-hour week almost doubled. Midnight fab shifts, weekend work, endless overtime. My startup didn’t die because the idea was bad. It died because I didn’t have the financial runway to compete. Here’s what that painful chapter taught me: 1. Passion + grit aren’t enough. Startups need time, capital, and breathing room to survive. 2. Corporate is just built for stability. It can’t nurture the creative chaos that startups need. 3. Adaptation beats stubbornness. Sometimes the smartest move is to regroup, save, and come back stronger. So I made a choice: doubled down on my W-2 career, moved to Seattle, and joined Amazon to maximize income. Every dollar earned—and every loan I could pull—went straight into real estate. One property a year turned into two… then into double digits in a single year. Deals kept coming, I kept closing, and I built a local team to keep momentum alive. By the end of 2022, my portfolio was generating more passive income than my paycheck. That’s when WoolooAI.com was born—not as a flashy startup, but as a necessary system to simplify and automate the chaos: acquisitions, CRM, portfolio management, project management, all streamlined. The truth is: - Without the grind of my early startup days, I wouldn’t have landed the Amazon role. - Without Intel’s layoff threats, I wouldn’t have learned the importance of building a financial runway first. - And without the expertise I developed at Amazon, I wouldn’t have gained the skills that helped me build WOOLOO AI. The irony isn’t lost: the same challenges that once crushed me ended up shaping the freedom I enjoy today. 💡 Takeaway: Sometimes what feels like an ending is really just the foundation for the next chapter. 👉 If you want the full story, I wrote it here: https://lnkd.in/geEVye36 💬 I’d love to hear your story too—what twists or setbacks ended up shaping where you are today? #Entrepreneurship #Startups #RealEstateInvesting #AmazonAlumni #FinancialFreedom #LessonsLearned #HumbleGrowth

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Waymo for Delivery Turns out the Waymo of delivery is --- DoorDash! DoorDash's Dot delivery bots are a BIG deal. They have an autonomy stack with sensors, hardware, and software similar to that of robotaxis, no extra fees to merchants or consumers (vertical integration is key for this IMO, standalone robot companies without demand cannot survive), are capable of going down busy streets. get better every single day with reinforcement learning from millions of deliveries, and have a much better form factor (not to mention much lower cost) than Waymo for delivery. Transporting humans is an expensive proposition and the robots should be built with this in mind; transporting your food and groceries should be designed from scratch with very different design principles. (eg: how does the vehicle behave in a crash? in the case of Waymo, it must protect its passengers. For autonomous delivery bots, they should sacrifice their food :)) This could very well be the Waymo moment for delivery. Congrats Stanley Tang and team! Can't wait to try it out soon 🙏 Link: https://lnkd.in/gECNPEQ9

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Most startups die from moving too fast. But plenty die from moving too slow. So how do you actually balance speed vs. technical debt? I sat down with Shuzhi Huang, former Engineering Leader at Google, someone who's seen this play out at massive scale, and got a remarkably honest answer. The insight that hit hardest: technical debt isn't the enemy. Unacknowledged technical debt is. If you're a founder scaling your engineering team right now, or a hiring manager trying to decide when to prioritize velocity over quality — this one's for you. What's your take? The full episode is live. Link in the comments 👇 #Engineering #TechnicalDebt #StartupFounders #EngineeringLeadership #Hiring #BotifySessions #BotifyTech #TechLeadership #Founders

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The Claude ecosystem is exploding faster than I expected. My newest favorite plugins: - Get Shit Done (orchestration layer that plans and builds apps phase by phase) - Ralph Wiggum Plugin ( iterative, self-referential AI development loops) - n8n-MCP (direct Claude integration with 1,000+ automation nodes) What's interesting is how quickly we've moved from simple AI coding assistants to full orchestration platforms. The shift feels similar to what happened with CI/CD tools a few years ago. First you had basic scripts, then comprehensive pipelines, now full DevOps orchestration. We're seeing the same evolution with AI coding tools, just compressed into months instead of years.  #AI #Claude #SoftwareDevelopment #AIAgents #DevTools #Automation

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Over the past year, my team and I built Pinterest's Model Context Protocol (MCP) ecosystem from the ground up: a central registry, a growing fleet of domain-specific MCP servers, and production integrations across our IDEs, internal chat surfaces, and AI agents. MCP is an open standard that gives large language models a unified way to talk to tools and data sources. At Pinterest, we used it as the substrate for AI agents that can safely automate real engineering tasks, from querying Presto data on demand to diagnosing Spark job failures and surfacing institutional knowledge. A few things I'm particularly proud of: * We designed for security from day one: two-layer auth (end-user JWTs + mesh identities), business-group-based access gating, and mandatory human-in-the-loop for sensitive actions. * We made it easy for any team to ship a new MCP server by creating a unified deployment pipeline that handles all the infrastructure, so domain experts just write business logic. * The ecosystem has scaled to 66,000+ invocations per month across 844 monthly active users, saving an estimated 7,000 engineering hours per month. This was a cross-functional effort. Thank you to everyone on the Agent Foundations, Security Engineering, and Traffic Engineering teams who made this possible, and to our engineering sponsors for their support and guidance. Full write-up on the Pinterest Engineering Blog: https://lnkd.in/e-mH26Bw

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I am incredibly proud to share my latest engineering blog on how our team at Roblox supported the system that broke the world record for most concurrent players. Roblox broke the record of supporting over 45M concurrent users in 2025. To handle this unprecedented traffic, we pushed our caching layer to a staggering 1.38 Billion QPS, completely bypassing standard Redis limits to keep the game world seamless and immersive. Check out the link below to see exactly how we architected for world-record scale! 👇 🔗 Read the full deep-dive here: https://lnkd.in/gTseqUuD

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Terminal Use (YC W26): Vercel for background agents. Terminal Use is hosting infrastructure for background agents that makes harness engineering easy. https://lnkd.in/gv99wN2t

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Honored to have been invited to fal and Henry Shi’s Lean AI Leaderboard Celebration Dinner, celebrating some of the leanest and fastest-growing startups in SF. Here are some of the most overwhelming trends that were discussed among high-growth companies working with small, high-output teams: 1/ Headcount is no longer used as a success metric. As Henry Ward said, “Hiring means we failed to execute and need help.” Headcount is not a flex measure anymore. People aren’t asking “How big is your team?” anymore. They’re asking “How many customers do you have?” 2/ They're finding ways to leverage AI across every function. Every engineer uses Cursor or Windsurf. Every GTM leader is in Clay. The most efficient teams are built AI-first across the board. Thank you to Henry Shi, Burkay Gur, and Gorkem Yurtseven for putting this all together!

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I took my second Waymo across Los Angeles last night, and the experience was largely excellent. From the smooth pickup to navigating the freeway (which is now supported here in LA!), the technology is undeniably impressive. However, the "last mile" wasn't quite perfect. I was dropped off on the left side of a two lane, one way street, directly in traffic rather than on the shoulder. Also, my destination was on the right. As I've written previously (https://lnkd.in/gHw7drrc), there is great promise in using technologies like AI in countless ways, from autonomous driving to talent measurement. However, my recent Waymo experience highlights two critical lessons: 1. Humans Must Own the Outcome: Whether you are a passenger in an autonomous vehicle or managing talent, you cannot delegate accountability to the machine. Technology is a powerful tool, but humans must own the final result. In hiring, as in transport, we must remain in the driver's seat to ensure the "drop off" (i.e. the final decision) is the right one. 2. The Feedback Loop is Non Negotiable: Without human feedback to these systems, technology will never improve. I made sure to provide feedback on my ride, and we must do the same in our talent systems. If we don’t provide constant, iterative feedback, we lose the opportunity to refine them into something better. Progress happens when human intuition meets technological scale. We provide the feedback; the tech provides the lift.

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Zoox on Tuesday began allowing select San Francisco users to hail its driverless vehicles, pitting the Amazon-owned robotaxi service against Alphabet’s Waymo in the same market for the first time.

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DeepSeek V3.1 just dropped. No press release. No hype cycle. Just showed up on Hugging Face. But look at these numbers: 71.6% on Aider coding benchmark. Better than Claude Opus at 68x lower cost. 66% on SWE-Bench. Competitive with frontier models. 128K context + hybrid thinking. One model, two modes. Here's what matters for production: Your entire codebase fits in one conversation. No more chopping context into fragments that break everything. Two modes, one model. Fast answers for simple stuff. Deep reasoning when you need it. Open source. 685B parameters. No vendor lock-in. Most "AI agents" are just chatbots with function calling duct-taped on. This is different. But here's my worry: DeepSeek has no branded agent framework like Claude Code. The open-source ecosystem will probably mess this up with bad prompts and cheap deployments. You'll see providers serving with lowered precision, pruned experts, poor sampling. Your experience will vary wildly based on where you deploy. Your reality check Week 1: Deploy locally Week 2: Build one autonomous task Document analysis. Code review. Data pipeline. Whatever. But test multiple providers. The benchmarks mean nothing if your deployment is garbage. This is probably as far as they can push V3. But for coding agents? This works. What are you building first?

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In a world of Agentic AI, the talent formula is clear 👇 🧮 Asking the right questions > Creating the answer The two most dangerous archetypes in the room? 1️⃣ Great PM's that moonlight as engineers 2️⃣ Great Engineers that don't know they are actually PM's #AI #AgenticAI #FutureOfWork #ProductManagement #Leadership #TechTalent

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Huge thanks to Zhe Jia, Shuai Wang, Aditi Bamba, and Martin Wang from DoorDash for publishing this incredibly practical breakdown of their RAG implementation for Dasher support. This post saved me countless hours of trial and error. The two-tier guardrail approach is what really clicked for me: * Run a cheap semantic similarity check first (sliding window comparison between response and KB articles) * Only if that fails, escalate to the full LLM evaluator Result: 90% fewer hallucinations, 99% fewer compliance issues, without killing your latency budget When you're actually shipping RAG to users, this kind of practical filtering makes all the difference. Everyone talks about prompt engineering, but nobody talks enough about how to catch bad outputs before users see them. Their broader setup—quality monitoring across 5 dimensions, regression test suites, the whole LLM Judge framework—gives you a real blueprint beyond the basic tutorials. You can't fix what you're not measuring. The article is https://lnkd.in/dbC9BG7a

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✨ A very fun start to Frameworks for Growth - a new video series from Vanta. "Frameworks" are core to who Vanta is: -We support 40 different compliance frameworks within the product -One of our principles is "Decide with frameworks," and a common question in meetings at Vanta is, "what framework did you use for deciding [x]?" -And now, we're hoping to provide founders and early operators with some frameworks *they* can use for scaling their businesses in the age of AI. Thanks to Garry Tan and Christina Cacioppo for this awesome first conversation. You should watch the whole thing, but tldw - one framework for growth: learn an industry deeply and vibe code towards something people want.

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DeepSeek’s rise has been wild to watch. From hedge fund roots to topping app store charts, all while running models cheaper and faster than the big names? That’s no small feat. Their focus on reasoning models is especially interesting and definitely shaking things up in the AI space. #AI #DeepLearning #Innovation #TechTrends #MachineLearning #Startups https://lnkd.in/dDfmM99D

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